mirror of
https://github.com/phuryn/pm-skills.git
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Pawel Huryn
100 lines
3.3 KiB
Markdown
100 lines
3.3 KiB
Markdown
---
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description: Perform cohort analysis on user data — retention curves, feature adoption, and engagement trends
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argument-hint: "<data file or description of what to analyze>"
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---
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# /analyze-cohorts -- Cohort Analysis
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Analyze user retention and engagement patterns by cohort. Upload your data or describe what you need, and get retention curves, feature adoption trends, and actionable insights.
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## Invocation
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```
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/analyze-cohorts [upload a CSV of user activity data]
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/analyze-cohorts Monthly retention for users who signed up in Jan-Jun, grouped by acquisition channel
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/analyze-cohorts Help me set up a cohort analysis for our onboarding redesign
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```
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## Workflow
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### Step 1: Accept Data or Define Analysis
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Two paths:
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- **With data**: User uploads a CSV/spreadsheet with user-level data (user_id, signup_date, activity_date, event_type, etc.)
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- **Without data**: User describes the analysis they need → generate the SQL query and analysis framework
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### Step 2: Define Cohorts
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Ask:
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- What defines a cohort? (signup week/month, acquisition channel, plan tier, first feature used)
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- What is the retention event? (login, core action, any activity, purchase)
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- What time granularity? (daily, weekly, monthly)
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- What time range?
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### Step 3: Analyze
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Apply the **cohort-analysis** skill:
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**If data is provided:**
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- Process the data using Python (pandas) to create cohort tables
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- Calculate retention rates per cohort per period
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- Generate retention curves
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- Identify patterns: improving/declining cohorts, seasonal effects, anomalies
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- Compare feature adoption across cohorts
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**If describing an analysis:**
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- Design the cohort analysis framework
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- Generate SQL queries to extract the data
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- Create a template spreadsheet for the analysis
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- Define the metrics and visualization approach
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### Step 4: Generate Report
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```
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## Cohort Analysis: [Description]
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**Date**: [today]
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**Cohort definition**: [e.g., signup month]
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**Retention event**: [e.g., completed a project]
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**Granularity**: [weekly/monthly]
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### Retention Table
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| Cohort | Size | Week 1 | Week 2 | Week 3 | ... | Week 12 |
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|--------|------|--------|--------|--------|-----|---------|
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### Key Findings
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1. **[Finding]** — [supporting data]
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2. ...
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### Cohort Comparison
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- **Best-performing cohort**: [which, why]
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- **Worst-performing cohort**: [which, why]
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- **Trend**: [improving/declining/stable over time]
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### Retention Benchmarks
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| Period | Your Rate | Industry Benchmark | Gap |
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|--------|----------|-------------------|-----|
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### Recommendations
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1. [What to investigate or change based on findings]
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2. ...
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### Follow-Up Queries
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[SQL queries for deeper investigation]
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```
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If data was provided, save analysis as both markdown report and CSV/spreadsheet.
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### Step 5: Offer Next Steps
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- "Want me to **segment this further** by another dimension?"
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- "Should I **set up metrics alerts** based on these retention thresholds?"
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- "Want me to **design experiments** to improve retention for the weakest cohort?"
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## Notes
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- Cohort analysis is only as good as the retention event definition — push for a meaningful action, not just "logged in"
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- Early cohorts often look different due to founding user bias — note this when comparing
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- If retention is calculated using a Python script, save the script so the user can re-run with new data
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- Seasonal effects can masquerade as trends — flag if cohort differences might be calendar-driven
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